@misc{10481/73940,
year = {2021},
month = {8},
url = {http://hdl.handle.net/10481/73940},
abstract = {Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input
for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility
of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we
propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis
(PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data.
The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and
then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is
presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function
fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN),
feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results
showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of
mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average
accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four
imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing
meteorological data into an understandable format for the missing data imputation.},
organization = {Ministry of Higher Education under the Fundamental Research Grant Scheme	FRGS/1/2018/ICT04/UTM/01/1},
organization = {Universiti Teknologi Malaysia (UTM) under Ministry of Higher Education Malaysia	20H04},
organization = {Malaysia Research University Network (MRUN) under Ministry of Higher Education Malaysia	4L876},
organization = {SLAI under Ministry of Higher Education Malaysia},
organization = {SPEV project, Faculty of Informatics and Management, University of Hradec Kralove	2102-2021
2102/2021},
organization = {Hradec Kralove University, Czech Republic},
publisher = {Universidad Internacional de La Rioja},
keywords = {Imputation},
keywords = {Missing Rainfall Data},
keywords = {Principal component analysis (PCA)},
keywords = {Sine cosine neural network},
keywords = {Deep learning},
title = {Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network},
doi = {10.9781/ijimai.2021.08.013},
author = {Chan Chiu, Po and Herrera Viedma, Enrique},
}